{"id":"W3214728753","doi":"10.1002/cjce.23999","title":"A parametric study through the modelling of hydrothermal gasification for hydrogen production from algal biomass","year":2020,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Subcritical and Supercritical Water Processes","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Biomass (ecology); Environmental science; Hydrogen production; Syngas; Process engineering; Yield (engineering); Raw material; Hydrogen; Process (computing); Pulp and paper industry; Moisture; Waste management; Chemistry; Materials science; Computer science; Ecology; Engineering; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001415404,0.0001140476,0.0001945381,0.00004198086,0.00004540867,0.00003023155,0.0002929925,0.00004937407,0.000009544559],"category_scores_gemma":[0.0003627694,0.00007394631,0.0000850209,0.0002919545,0.00005117489,0.0001220961,0.000006997227,0.0002249599,0.000001322142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005077586,"about_ca_system_score_gemma":0.00005508025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004356157,"about_ca_topic_score_gemma":0.00001863423,"domain_scores_codex":[0.9991775,0.00001249197,0.0003467743,0.00009158171,0.0001556343,0.0002160688],"domain_scores_gemma":[0.9993989,0.0001833826,0.00001982785,0.0001085796,0.0001062845,0.0001829608],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000296299,0.0000274658,0.0001137256,0.0001621959,0.0002003142,0.000007679952,0.003498873,0.8281918,0.1669469,0.0002847867,0.00008392327,0.0004527392],"study_design_scores_gemma":[0.0002328013,0.00005805303,0.0000174236,0.00003614694,0.00009332499,0.00001332997,0.000169222,0.4247323,0.5734177,0.000900865,0.0002072539,0.0001216374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9618995,0.0008006808,0.03529466,0.00161992,0.0001270626,0.0002057398,0.00001402424,0.00002244872,0.00001592844],"genre_scores_gemma":[0.9987687,0.0000025994,0.0007784528,0.00004112109,0.0003673629,0.0000127058,0.000002180451,0.00002616127,6.605025e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4064708,"threshold_uncertainty_score":0.3015443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03890892374980112,"score_gpt":0.2076290942125537,"score_spread":0.1687201704627526,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}